Generative AI

Generative models learn patterns from very large datasets — patterns in language, images or code — and use them to create new, plausible content. A language model essentially predicts the most likely next word; from this simple principle emerges the ability to answer questions, summarise texts or draft emails.

The flip side of this principle: the model generates plausible text, not verified facts. Without a connection to real data sources, generative AI can produce convincing errors (AI hallucination) — which is why data integration (RAG) and human approval belong in every professional deployment.

In everyday business operations, generative AI rarely takes centre stage visibly. It works behind the scenes in AI agents: reading incoming documents, drafting proposals and responses, summarising conversations or translating between system formats. People see the output, not the model.

Generative AI isn't limited to text. Text models write and summarise, image models generate graphics and product visualisations, audio models transcribe conversations or generate natural speech output (the basis of AI phone assistants), and code models significantly accelerate software development. In mid-market companies, text processing clearly dominates — because that's where most recurring office work happens.

Increasingly, these forms work together: a system transcribes a customer call (audio to text), summarises the issue (text), creates a CRM entry and a draft response. These multi-stage chains are the core of modern AI agents — generative AI is the engine here, not the product.

Anyone using generative AI commercially should keep a few legal points in view — not as obstacles, but as part of responsible operation. On copyright: purely machine-generated content generally enjoys no independent copyright protection under prevailing legal opinion, because there's no human authorship. For businesses, this means purely AI-generated texts or images may not be exclusively protected — something worth considering for core brand or marketing assets.

Conversely, you need to check whether generated content infringes third-party rights. An image model can produce results that resemble protected works or trademarks; a text model can reproduce others' phrasing. For content with external reach, human review is therefore essential, just as it would be for externally sourced texts.

The third point is disclosure: the EU AI Act requires AI-generated or substantially altered content to be identifiable as such in certain cases. For most office applications with human approval, this is straightforward; for widely published content, a deliberate decision on transparency is worth making.